Motivated by bidders' interests in concealing their private information in the auction processes, we introduce a new ascending clock auction for assignment problems that better preserves bidder privacy by economizing on information revealed about bidders' valuations. Our auction uses a progressive partial reporting design, where we judiciously query subsets of bidders and ask a marginal bidder to submit a single new bid at a time instead of his entire demand set. In this auction, despite partial reporting, sincere bidding is an ex-post Nash equilibrium, ending prices are path independent, and efficiency is achieved if starting prices equal to the auctioneer's reservation values. We also propose a new, general-purpose measure of bidder information revelation based on Shannon's entropy, together with a hybrid quasi-Monte Carlo procedure for computing such a measure. Our numerical simulation shows that our auction consistently outperforms a full-reporting benchmark by up to 18% less entropy reduction, and scales to problems of over 100,000 variables.

BIO: My general research interests lie in combining economic thinking with sociological and psychological perspectives in analyzing and designing mechanisms for digital markets and platforms. His current research deals with economics of Internet auctions & contests, gamification, social media and social commerce, crowdfunding and Internet finance. My research has appeared in outlets such as MIS Quarterly, Information Systems Research, Journal of Marketing, Journal of Market Research, Production and Operations Management. I currently serves as an associate editor for Information Systems Research and Journal of Organizational Computing and Electronic Commerce. I teach big data and business analytics courses at Carlson School, and serve as the Program Director of the MS in Business Analytics, and PhD coordinator of Information and Decision Sciences.